A first technology has been proposed in which a load situation of a system that runs on a virtual server is acquired from a plurality of physical servers and a load that is applied to the physical server is allocated according to a genetic algorithm that expresses a load capacity of the system that runs on the virtual server, in a gene arrangement. In the first technology, the allocated load is distributed to a plurality of physical servers.
Furthermore, a second technology also has been proposed in which a service transaction repository that is associated with at least two service transactions which are the same in function is decided and each of the service transactions is associated with at least one data characteristic. In the second technology, a data characteristic pattern in the service transaction repository is decided and a service map that associates the decided data characteristic pattern with a service is decided.
Furthermore, a third technology also has been proposed in which accurate and sufficient maintenance support information for setting up a plan for apparatus maintenance is provided. In the third technology, the number of estimated remaining apparatuses in each elapsed period of time is calculated from an estimated remaining rate and the number of apparatuses installed in the past, an estimated hazard function is calculated from the number of estimated remaining apparatuses and the number of repairs made in the past to calculate an estimated accumulation repair rate, and a prediction accumulation repair rate is calculated by applying a prediction model using data within a learning period of time.
Patent Document 1: Japanese Laid-open Patent Publication No. 2008-269250
Patent Document 2: Japanese Laid-open Patent Publication No. 2006-268848
Patent Document 3: Japanese Laid-open Patent Publication No. 2013-114636
For example, like the third technology, a technology is known in which models for determining occurrence events are generated by learning for every attribute of event information, from a plurality of pieces of event information that represent the occurrence events, respectively, and in which the event is determined by applying a corresponding model from event information after generation of the models. In this technology, in a case where the event information occurs from moment to moment, or in a case where real time is requested for determination of a state of the event, for example, a plurality of processing apparatuses, such as servers, are provided, models different from each other are allocated to the plurality of processing apparatuses, and thus processing is distributed to the plurality of processing apparatuses. It is noted that the processing apparatus includes a CPU (a processor), a memory, a hard disk, a display, a communication interface, and an input device such as a keyboard.
Furthermore, in the field of the technology described above, with application of a machine learning technology, there has been advancement in subdivision of an attribute of the event information that is a unit of model generation, and there is a tendency to determine a state of an occurrence event by selectively applying a model that corresponds to the attribute of the event information, from many more models. As an example of determination of abnormality in user's operation of a terminal, a model for every user who operates the terminal is generated, but for example, models for every user, for every terminal, for every type of days (workday or off day), and for every operating time zone are generated. Thus, an improvement in precision of the abnormality determination is expected. Also in a case where an attribute of the event information is subdivided, because the number of models increases, a configuration is employed in which processing is distributed to a plurality of processing apparatuses.
However, even the processing is distributed by allocating models different from each other to a plurality of processing apparatuses, because which model is to be applied to the determination of the state of the event that is indicated by the occurrence event information is decided according to the attribute of the event information, it is not guaranteed that the frequency of usage of each model is uniform. For this reason, there may be a case where deviation occurs among loads on a plurality of processing apparatuses due to deviation of the frequency of usage of the model and the load on any one of the processing apparatus exceeds an allowable amount.
In theory, it is possible that this problem is solved, if each of the processing apparatuses is made to be able to perform processing using any one of the models, by storing all models in a storage unit mounted in each of the processing apparatuses, which is capable of storing all models. However, it is not realistic to make a built-in memory the storage capacity described above while the number of models tends to increase. Furthermore, it is considered that the allocation of the model to each processing apparatus is adjusted based on the frequency with which each model is used in the past, in such a manner that the load is averaged, but because it is not guaranteed that a future frequency with which each model will be used is the same as in the past, it is difficult to solve the problem described above. Then, it is difficult for the first to third technologies described above to solve the problem of distributing the load to the processing apparatus and suppressing the load from exceeding the amount of allowance without increasing the capacity of each of the processing apparatus to store the model.